![]() ![]() Experimental results on UrbanSound8K datasets demonstrate that the proposed CNN-RNN architecture achieves better performance than the state-of-the-art classification models. The results show that the generated images by DCGAN have similar features to the original training images and has the capability to generate spectrograms and improve the classification accuracy. Batch normalization, transfer learning, and three feature representations map are used to improve the model accuracy. This data augmentation technique is applied to the UrbanSound8K dataset to improve the environmental sound classification. ![]() ![]() Moreover, a Deep Convolutional Generative Adversarial Network (DCGAN) is used for high-quality data augmentation. Aperture Radar Image Synthesis by Using Generative. Generative adversarial networks (GAN) have shown promise in data generation application in the fields of image and audio processing. To aid in the creation of improved machine learning-based ship detec-tion and discrimination methods this paper applies a type of neural network known as an Information Maximizing Gen-erative Adversarial Network. As such, this paper proposes the design of a GAN for. Generative adversarial networks (GAN) have shown promise in data generation application in the fields of image and audio processing. In this paper, a recurrent neural network (RNN) combined with CNN is proposed to address this problem. Generative Adversarial Networks (GANs) has been influenced numerous fields since. data such as Synthetic Aperture Radar imagery. A major obstacle in radar based methods for concealed object detection on humans and seamless integration into security and access control system is the difficulty in collecting high quality radar signal data. Also, the main drawback of deep learning algorithms is that they need a huge number of datasets to indicate their efficient performance. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of. While convolutional neural networks (CNNs) have shown great success in feature extraction and audio classification, it is important to note that real-time audios are dependent on previous scenes. generative adversarial networks (gan) have shown promise in data generation application in the fields of image and audio processing. Deep neural networks in deep learning have been widely demonstrated to have higher accuracy and distinct advantages over traditional machine learning methods in extracting data features. Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. A major obstacle in radar based methods for concealed object detection on humans and seamless integration into security and access control system is the difficulty in collecting high quality radar signal data. ![]()
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